基于多重稀疏字典的行人检测方法

Pedestrian detection method based on multiple sparse dictionaries

针对于稀疏编码在行人检测问题中提取的特征维数高和不能够有效描述行人的问题,提出了一种基于多重稀疏字典直方图的特征提取方法。通过稀疏表示方法,预先学习多个不同稀疏度的字典,分别利用每一个字典对行人图像进行稀疏编码,统计每个字典中对应稀疏编码单元的分布直方图作为行人图像的特征描述子。该方法提取到的特征维数低,并且能够有效地描述行人,具有良好的检测性能。

The drawbacks of pedestrian detection method based on sparse code are high dimensions of features and can’t ef-fectively describe the pedestrian. Aiming at the drawbacks,a feature extraction method based on multiple sparse dictionaries his-togram is proposed. Several different sparse dictionaries need to be learned before hand by means of the sparse representation, sparse coding of pedestrian image is conducted with different sparse dictionaries to make statistics of the distribution histograms corresponding to the sparse coding units in each dictionary as the image feature descriptor. The feature dimensions extracted with this method are low,can effectively describe the pedestrian,and has good detection performance.